1. Technical Field
This invention relates to the field of network management and in particular the management of complex data communication networks.
2. Related Art
Communication networks such as the Internet are probably the most complex machines built by mankind. The number of possible failure states in a major network is so large that even counting them is infeasible. Deciding the state that the network is in at any time with great accuracy is therefore not possible. In addition, data networks such as the Internet are subjected to a mixture of deterministic and stochastic load (see V Paxson and S Floyd, “Wide Area Traffic: The Failure of Poisson Modelling”, IEEE/ACM Transactions on Networking 3, (3), pp 226-244, 1995 & S Gribble and E Brewer, “System Design Issues for Internet Middleware Services: Deductions from a Large Client Trace”, Proceedings of the USENIX Symposium on Internet Technologies and Systems (USITS ′97), December 1997). The network's response to this type of traffic is chaotic (see M Abrams et al, “Caching Proxies: Limitations and Potentials”, Proc. 4th Inter. World-Wide Web Conference, Boston, Mass., December 1995), and thus the variation of network state is highly divergent and accurate predictions of network performance require knowledge of the current network state that is more accurate than can be obtained. Future networks, which will have increased intelligence, will be even more complex and have less tractable management. A network management paradigm is required that can maintain network performance in the face of fractal demands without detailed knowledge of the state of the network, and can meet unanticipated future demands.
Biologically inspired algorithms (for example genetic algorithms and neural networks) have been successfully used in many cases where good solutions are required for difficult (here, the term ‘difficult’ is used to represent a problem that is computationally infeasible using brute force methods) problems of this type (see CM Roadknight et al, “Modelling of complex environmental data”, IEEE Transactions on Neural Networks. Vol. 8, No 4. P. 852-862, 1997 & D Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, 1989). They simulate evolutionary procedures or neural activation pathways in software, these then acting as problem solving tools. They can do this because they take a clean sheet approach to problem solving, they can learn from successes and failures and due to multiple adaptive feedback loops, they are able to find optima in a fractal search space quickly.